Sergey Nivens - Fotolia
The right data can help your company unlock hidden insights, gain competitive edge, develop new products, make killer business decisions -- the list goes on and on. However, realizing anything close to those promises is no easy task and becoming a data-driven company is key.
Here are six essentials to help you get there.
1. Ensure that your data and information can be trusted.
A data-driven company can only be built on trustworthy information. Indeed, underlying data quality is a prerequisite for information integrity, since the principle of "garbage in-garbage out" applies.
Likewise, the data analysis and presentation processes are equally as important as the quality of the raw data. For example, get the data selection logic wrong, and the end result will be incorrect, despite the fact that the underlying data was 100% accurate in every respect.
Takeaway: Establish robust and effective data integrity and data processing validation processes.
2. Get a handle on the unstructured data within your organization.
Other than the structured data residing within your organization's primary transactional systems (e.g., ERP or CRM systems), you will need to know what business value exists in the unstructured data housed within your organization.
Typically, unstructured data comprise spreadsheets, databases, ad-hoc data extracts, Word documents, multimedia files and other data-related content. These repositories are often spread across a range of locations, from file servers, PCs and laptops, stand-alone cloud accounts, removable media, and mobile devices.
Takeaway: A critical success factor in the shift to becoming a data-driven company is to ensure that you are able to access, interpret and extract the business value from all data and information held within your organization -- including unstructured data.
3. Apply data science principles when analyzing and interpreting your data.
Data science is an emerging discipline in the new world of IT, and it's one that is crucial to resolving complex data problems that relate to transforming data into knowledge and information. Data science is the result of drawing together a range of specialized skills to work on data, which include applied mathematics and statistics, computer science, pattern recognition, machine learning, natural language processing and operations research, to name but a few. Data science should be seen as an important complementary skill to those of information architecture or business analysis.
Takeaway: Recognize the value that the discipline of data science can bring to your company, and then make that investment in applying these skills.
4. Use real-time data to drive business processes automatically, where feasible.
Adding data-based automation is a key factor in becoming a data-driven company. This will increase the speed with which actions can be taken, eliminate waste and minimize human error. For example, retail stores that automatically link their stock ordering processes to the point of sale -- in real time -- is one well-known example of a data-driven process. However, ensure that you have tested these automated processes carefully.
Takeaway: Understand what can be automated by using data and what cannot. Human experience and insights still have their place. Know the difference.
5. Change your organization's culture to support data-driven processes.
Becoming a data-driven company requires your company's leaders and employees to view their jobs differently and make decisions that increasingly revolve around multidisciplinary collaboration. For example, while salespeople may understand their customer's sales histories well, insights provided by others outside the sales team could help redefine the sales value proposition, resulting in the creation of new opportunities and markets.
Indeed, manipulating integrated, companywide data to drive actionable business decisions requires a multidisciplinary and collaborative approach in the design and implementation of the business logic to minimize the risk of the wrong assumptions being made. That collaborative approach can be at odds with conventional structures based around functional expertise, such as accounting, marketing, shipping, HR, warehousing or sales.
Takeaway: Becoming a data-driven company requires a cultural shift toward cross-functional collaboration.
6. Review and adjust individual incentives.
Incentives drive leadership and staff behaviors. In transitioning to a data-driven organization, it is important to ensure that individual incentives reflect the individual contribution to the overall success of the initiative. In doing so, all stakeholders have a shared interest in working with others across the organization to ensure that the overall process works as expected. This helps prevent the "it's not my job" effect.
Takeaway: Ensure individual goals and targets accurately reflect contributions to ensuring the overall successful transition to becoming a data-driven company.
In a data-driven company, decision-making processes throughout the entire organization -- be they strategic, tactical or operational -- are heavily reliant, if not absolutely dependent, on hard data. Any company that makes this transition successfully is sure to drive sustained organizational value. Recognizing the limited part that technology plays in this journey is the first step in the right direction.
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